, Volume 9, Issue 3, pp 399–416 | Cite as

Evolutionary Biosemiotics and Multilevel Construction Networks

  • Alexei A. SharovEmail author


In contrast to the traditional relational semiotics, biosemiotics decisively deviates towards dynamical aspects of signs at the evolutionary and developmental time scales. The analysis of sign dynamics requires constructivism (in a broad sense) to explain how new components such as subagents, sensors, effectors, and interpretation networks are produced by developing and evolving organisms. Semiotic networks that include signs, tools, and subagents are multilevel, and this feature supports the plasticity, robustness, and evolvability of organisms. The origin of life is described here as the emergence of simple self-constructing semiotic networks that progressively increased the diversity of their components and relations. Primitive organisms have no capacity to classify and track objects; thus, we need to admit the existence of proto-signs that directly regulate activities of agents without being associated with objects. However, object recognition and handling became possible in eukaryotic species with the development of extensive rewritable epigenetic memory as well as sensorial and effector capacities. Semiotic networks are based on sequential and recursive construction, where each step produces components (i.e., agents, scaffolds, signs, and resources) that are needed for the following steps of construction. Construction is not limited to repair and reproduction of what already exists or is unambiguously encoded, it also includes production of new components and behaviors via learning and evolution. A special case is the emergence of new levels of organization known as metasystem transition. Multilevel semiotic networks reshape the phenotype of organisms by combining a mosaic of features developed via learning and evolution of cooperating and/or conflicting subagents.


Evolutionary semiotics Constructivism Semiotic network Evolvability Metasystem transition Constraints on learning 



This paper was supported entirely by the Intramural Research Program of the National Institute on Aging (NIA/NIH), project Z01 AG000656-13. The content of the paper is not endorsed by the funding organization.


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© US Government 2016

Authors and Affiliations

  1. 1.National Institute on Aging, Laboratory of GeneticsBaltimoreUSA

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